feat: use file-based SQLite database for persistent failure tracking

- Target single GPU (ppu_zw_810e) instead of 4 GPUs
- Search only from HuggingFace API (removed ModelScope)
- Framework fixed to vllm (removed llama.cpp)
- Filter out GGUF format models
- Simplified config and functions

Dockerfile

@@ -4,6 +4,8 @@ ENV PYTHONUNBUFFERED=1
 
 WORKDIR /app
 
+RUN mkdir -p /app/data
+
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 

main.py

@@ -30,33 +30,11 @@ HOST = "0.0.0.0"
 PORT = 8080
 STRATEGY_ID = os.getenv("STRATEGY_ID", "")
 
-# 四个账号的 Token
+# 目标GPU
-ACCOUNTS = {
+TARGET_GPU = "Iluvatar_bi-150"
-    'MetaX_c-500': 'f8e60d1dac7f4472967e7ca40145747b',
-    'Kunlunxin_p-800': 'f45f1aae2c094426be237c88b1085015',
-    'Ascend_910-b4': 'f3c05879e7c34bbba92f399f12884183',
-    'hygon_k100-ai': 'b88507029b884ad3b4bad8ba09e6546e',
-}
 
-# GPU 引擎配置
+# 账号Token
-GPU_CONFIGS = {
+TARGET_TOKEN = "f45f1aae2c094426be237c88b1085015"
-    'MetaX_c-500': {
-        'framework': 'vllm',
-        'docker_image': 'modelhubxc-4pd.tencentcloudcr.com/enginex/enginex-metax/vllm:0.9.1',
-    },
-    'Kunlunxin_p-800': {
-        'framework': 'vllm',
-        'docker_image': 'modelhubxc-4pd.tencentcloudcr.com/enginex/sunjichen/xc-llm-kunlun:latest',
-    },
-    'Ascend_910-b4': {
-        'framework': 'vllm',
-        'docker_image': 'git.modelhub.org.cn:9443/enginex-ascend/vllm-ascend:v0.11.0rc0',
-    },
-    'hygon_k100-ai': {
-        'framework': 'llama.cpp',
-        'docker_image': 'modelhubxc-4pd.tencentcloudcr.com/enginex/enginex-hygon/hygon-llama.cpp:b7516',
-    },
-}
 
 # 架构白名单
 SUPPORTED_ARCH_KEYWORDS = ['Qwen', 'Qwen2', 'Qwen3']
@@ -66,7 +44,6 @@ SUPPORTED_MODEL_TYPES = [
 ]
 SUPPORTED_SPECIAL_ARCHS = ['Eagle3Speculator', 'LlamaForCausalLMEagle3']
 
-MODELSCOPE_API = "https://modelscope.cn/api/v1"
 MODELHUB_API = "https://modelhub.org.cn/api"
 
 # 搜索关键词
@@ -104,41 +81,79 @@ db_conn = None
 
 def init_db():
     global db_conn
-    db_conn = sqlite3.connect(':memory:', check_same_thread=False)
+    os.makedirs('/app/data', exist_ok=True)
-    db_conn.execute('''CREATE TABLE IF NOT EXISTS queue (
+    db_conn = sqlite3.connect('/app/data/submit_history.db', check_same_thread=False)
-        model_id TEXT, gpu TEXT, url TEXT, downloads INTEGER,
+    db_conn.execute('''CREATE TABLE IF NOT EXISTS submitted (
-        params TEXT, category TEXT, score REAL,
+        model_id TEXT, gpu TEXT, task_id TEXT, status TEXT,
+        submitted_at TEXT, checked_at TEXT,
         PRIMARY KEY(model_id, gpu)
     )''')
-    db_conn.execute('''CREATE TABLE IF NOT EXISTS submitted (
+    db_conn.execute('''CREATE TABLE IF NOT EXISTS failed (
-        model_id TEXT, gpu TEXT, task_id TEXT, submitted_at TEXT,
+        model_id TEXT, gpu TEXT, reason TEXT, failed_at TEXT,
         PRIMARY KEY(model_id, gpu)
     )''')
     db_conn.commit()
+def is_model_failed(model_id: str) -> bool:
+    """检查模型是否已知失败"""
+    if db_conn:
+        row = db_conn.execute(
+            'SELECT 1 FROM failed WHERE model_id=? AND gpu=?',
+            (model_id, TARGET_GPU)
+        ).fetchone()
+        return row is not None
+    return False
+
+def record_failed(model_id: str, reason: str):
+    """记录失败的模型"""
+    if db_conn:
+        db_conn.execute(
+            'INSERT OR REPLACE INTO failed VALUES (?,?,?,?)',
+            (model_id, TARGET_GPU, reason, datetime.now().isoformat())
+        )
+        db_conn.commit()
 
 
 # ============================================================
 # ModelScope 搜索
 # ============================================================
 
-def search_models(keyword: str, limit: int = 100) -> list:
+MODELSCOPE_API = "https://modelscope.cn/api/v1"
-    """从 ModelScope 搜索模型"""
+DOWNLOAD_MIN = 50
+DOWNLOAD_MAX = 5000
+SEARCH_PAGES = 5  # 每个关键词搜5页（50*5=250个结果）
+
+
+def search_models(keyword: str) -> list:
+    """从 ModelScope 搜索模型（多页，筛选下载量50-5000的冷门模型）"""
     url = "https://modelscope.cn/openapi/v1/models"
-    params = {
+    models = []
-        'search': keyword,
+    for page in range(1, SEARCH_PAGES + 1):
-        'page_size': limit,
+        params = {
-        'page_number': 1,
+            'search': keyword,
-        'sort': 'downloads',
+            'page_size': 50,
-    }
+            'page_number': page,
-    try:
+            'sort': 'downloads',
-        resp = requests.get(url, params=params, timeout=20,
+        }
-                            headers={'User-Agent': 'Mozilla/5.0'})
+        try:
-        data = resp.json()
+            resp = requests.get(url, params=params, timeout=20,
-        if data.get('success'):
+                                headers={'User-Agent': 'Mozilla/5.0'})
-            return data.get('data', {}).get('models', [])
+            data = resp.json()
-    except Exception as e:
+            if data.get('success'):
-        log(f"  搜索失败 [{keyword}]: {e}")
+                page_models = data.get('data', {}).get('models', [])
-    return []
+                for m in page_models:
+                    dl = m.get('downloads', 0)
+                    if DOWNLOAD_MIN <= dl <= DOWNLOAD_MAX:
+                        models.append({'id': m.get('id'), 'downloads': dl})
+                if len(page_models) < 50:
+                    break  # 最后一页，不继续
+            else:
+                break
+        except Exception as e:
+            log(f"  [{keyword}] page={page}: {e}")
+            break
+        time.sleep(0.3)
+    log(f"  [{keyword}]: {len(models)} 个 (50<={DOWNLOAD_MAX})")
+    return models
 
 
 def check_architecture(model_id: str) -> tuple:
@@ -185,26 +200,27 @@ def normalize_model_url(model_url: str) -> str:
 
 def check_platform_verify(model_id: str) -> dict:
     """查询全平台验证状态"""
-    headers = {'Xc-Token': list(ACCOUNTS.values())[0], 'Accept': 'application/json'}
+    headers = {'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'}
     url = f"{MODELHUB_API}/computility/models/search-by-model-id"
     try:
         resp = requests.get(url, headers=headers, params={'modelId': model_id}, timeout=10)
         data = resp.json()
         if data.get('code') == 0:
-            return data.get('data', {}).get('verifyResult', {})
+            result = data.get('data', {}).get('verifyResult', {})
+            return result if isinstance(result, dict) else {}
     except Exception:
         pass
     return {}
 
 
-def check_my_submitted(model_id: str, gpu: str, token: str) -> bool:
+def check_my_submitted(model_id: str) -> bool:
     """检查自己是否已提交"""
-    headers = {'Xc-Token': token, 'Accept': 'application/json'}
+    headers = {'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'}
     url = f"{MODELHUB_API}/adapt/task/page"
     try:
         resp = requests.get(url, headers=headers, params={
             'current': 1, 'pageSize': 100, 'onlyMine': 'true',
-            'gpuType': gpu, 'modelId': model_id,
+            'gpuType': TARGET_GPU, 'modelId': model_id,
         }, timeout=10)
         data = resp.json()
         if data.get('code') == 0:
@@ -219,14 +235,14 @@ def check_my_submitted(model_id: str, gpu: str, token: str) -> bool:
     return False
 
 
-def check_queue_available(gpu: str, token: str) -> int:
+def check_queue_available() -> int:
     """查询队列可用位置"""
-    headers = {'Xc-Token': token, 'Accept': 'application/json'}
+    headers = {'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'}
     url = f"{MODELHUB_API}/adapt/task/page"
     try:
         resp = requests.get(url, headers=headers, params={
             'current': 1, 'pageSize': 1, 'onlyMine': 'true',
-            'gpuType': gpu, 'status': 'waiting',
+            'gpuType': TARGET_GPU, 'status': 'waiting',
         }, timeout=10)
         data = resp.json()
         if data.get('code') == 0:
@@ -237,104 +253,58 @@ def check_queue_available(gpu: str, token: str) -> int:
     return -1
 
 
-def build_config_params(gpu: str) -> str:
+def build_config_params() -> str:
-    """构建 YAML 配置"""
+    """构建 YAML 配置 - 完全匹配平台自动生成的格式（只支持vllm）"""
-    config = GPU_CONFIGS.get(gpu, {})
+    params = {
-    framework = config.get('framework', 'vllm')
+        'framework': 'vllm',
-    docker_image = config.get('docker_image', '')
+        'nv_framework': 'vllm',
-
+        'api': 'completion',
-    if framework == 'llama.cpp':
+        'max_tokens': 1024,
-        params = {
+        'temperature': 0.7,
-            'framework': 'llama.cpp',
+        'repetition_penalty': 1.2,
-            'docker_image': docker_image,
+        'top_p': 0.9,
-            'nv_docker_image': docker_image,
+        'lang': 'zh',
-            'api': 'completion',
+        'max_model_len': 2048,
-            'max_tokens': 1024,
+        'sut_config': {
-            'temperature': 0.7,
+            'gpu_num': 1,
-            'repetition_penalty': 1.2,
+            'values': {
-            'top_p': 0.9,
+                'command': [
-            'lang': 'zh',
+                    'vllm', 'serve', '/model', '--port', '8000',
-            'max_model_len': 4096,
+                    '--served-model-name', 'llm', '--max-model-len', '2048',
-            'modelFormat': 'GGUF',
+                    '--dtype', 'auto', '--gpu-memory-utilization', '0.95',
-            'sut_config': {
+                    '-tp', '1', '--enforce-eager', '--trust-remote-code',
-                'values': {
+                ]
-                    'gpu_num': 1,
+            }
-                    'command': [
+        },
-                        '/bin/bash', '-ic',
+        'ref_config': {
-                        'llama-server --model /model --alias llm --threads 20 '
+            'gpu_num': 1,
-                        '--n-gpu-layers 999 --prio 3 --min_p 0.01 '
+            'values': {
-                        '--ctx-size 4096 --host 0.0.0.0 --port 8000 --jinja --flash-attn off'
+                'command': [
-                    ]
+                    'vllm', 'serve', '/model', '--port', '80',
-                }
+                    '--served-model-name', 'llm', '--max-model-len', '4096',
-            },
+                    '--enforce-eager', '--trust-remote-code', '-tp', '1',
-            'ref_config': {
+                ]
-                'values': {
+            }
-                    'cpu_num': 2, 'gpu_num': 1,
+        },
-                    'command': [
+    }
-                        'llama-server', '--model', '/model', '--alias', 'llm',
+    return yaml.dump(params, default_flow_style=False, allow_unicode=True, width=1000)
-                        '--threads', '20', '--n-gpu-layers', '999',
-                        '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000',
-                    ]
-                }
-            },
-            'model': 'llm',
-        }
-    else:
-        params = {
-            'framework': 'vllm',
-            'docker_image': docker_image,
-            'nv_docker_image': docker_image,
-            'api': 'completion',
-            'max_tokens': 1024,
-            'temperature': 0.7,
-            'repetition_penalty': 1.2,
-            'top_p': 0.9,
-            'lang': 'zh',
-            'max_model_len': 2048,
-            'modelFormat': 'HuggingFace',
-            'sut_config': {
-                'values': {
-                    'gpu_num': 1,
-                    'command': [
-                        '/bin/bash', '-ic',
-                        'vllm serve /model --port 8000 --served-model-name llm '
-                        '--max-model-len 2048 --dtype auto --gpu-memory-utilization 0.95 '
-                        '-tp 1 --enforce-eager --trust-remote-code'
-                    ]
-                }
-            },
-            'ref_config': {
-                'values': {
-                    'cpu_num': 2, 'gpu_num': 1,
-                    'command': [
-                        'vllm', 'serve', '/model', '--port', '8000',
-                        '--served-model-name', 'llm', '--max-model-len', '2048',
-                        '--dtype', 'auto', '--gpu-memory-utilization', '0.95',
-                        '-tp', '1', '--enforce-eager', '--trust-remote-code',
-                    ]
-                }
-            },
-            'model': 'llm',
-        }
-    return yaml.dump(params, default_flow_style=False, allow_unicode=True)
 
 
-def submit_model(model_url: str, gpu: str, token: str) -> tuple:
+def submit_model(model_url: str) -> tuple:
     """提交单个模型"""
     headers = {
-        'Xc-Token': token,
+        'Xc-Token': TARGET_TOKEN,
         'Accept': 'application/json',
         'Content-Type': 'application/json',
     }
     url = f"{MODELHUB_API}/adapt/task/add"
-    config = GPU_CONFIGS.get(gpu, {})
     payload = {
         'modelAddress': normalize_model_url(model_url),
         'taskType': 'text-generation',
-        'targetGpu': gpu,
+        'targetGpu': TARGET_GPU,
-        'framework': config.get('framework', 'vllm'),
+        'framework': 'vllm',
         'strategyId': STRATEGY_ID,
-        'configParams': build_config_params(gpu),
+        'configParams': build_config_params(),
     }
     try:
         resp = requests.post(url, headers=headers, json=payload, timeout=30)
@@ -351,117 +321,129 @@ def submit_model(model_url: str, gpu: str, token: str) -> tuple:
 # 主流程
 # ============================================================
 
-def run_pipeline(gpus: list = None, submit_limit: int = 30):
+def run_pipeline(submit_limit: int = 2):
-    """完整流程：搜索→筛选→提交"""
+    """完整流程：搜索→筛选→提交（只针对目标GPU）"""
-    if gpus is None:
-        gpus = list(GPU_CONFIGS.keys())
-
     init_db()
     log("=" * 50)
     log("开始执行流程")
-    log(f"目标GPU: {', '.join(gpus)}")
+    log(f"目标GPU: {TARGET_GPU}")
-    log(f"提交限制: 每GPU {submit_limit} 个")
+    log(f"提交限制: {submit_limit} 个")
 
     # 1. 搜索
     log("\n--- 阶段1: 搜索 ModelScope ---")
     seen = set()
     all_models = []
     for kw in SEARCH_KEYWORDS:
-        models = search_models(kw, limit=100)
+        models = search_models(kw)
         for m in models:
             mid = m.get('id', '')
             if mid and mid not in seen:
                 seen.add(mid)
-                downloads = m.get('downloads', 0)
+                all_models.append({
-                if downloads >= 50:
+                    'model_id': mid,
-                    all_models.append({
+                    'url': f"https://modelscope.cn/{mid}",
-                        'model_id': mid,
+                    'downloads': m.get('downloads', 0),
-                        'url': f"https://modelscope.cn/{mid}",
+                })
-                        'downloads': downloads,
-                        'params': m.get('params', ''),
-                        'category': 'quantized' if 'GGUF' in mid.upper() else 'standard',
-                    })
         time.sleep(0.3)
-    log(f"搜索完成: {len(seen)} 个唯一模型, {len(all_models)} 个下载量>=50")
+    log(f"搜索完成: {len(seen)} 个唯一模型, {len(all_models)} 个下载量{DOWNLOAD_MIN}-{DOWNLOAD_MAX}")
 
-    # 2. 架构筛选
+    # 2. 格式筛选（排除 GGUF/GPTQ/AWQ）
-    log("\n--- 阶段2: 架构筛选 ---")
+    log("\n--- 阶段2: 格式筛选 ---")
+    hf_models = []
+    format_skipped = 0
+    SKIP_FORMATS = ['GGUF', 'GPTQ', 'AWQ']
+    for m in all_models:
+        mid_upper = m['model_id'].upper()
+        skip = False
+        for fmt in SKIP_FORMATS:
+            if fmt in mid_upper:
+                format_skipped += 1
+                log(f"  x {m['model_id']}: {fmt}格式，跳过")
+                skip = True
+                break
+        if not skip:
+            hf_models.append(m)
+    log(f"格式筛选: {len(hf_models)} 通过, {format_skipped} 跳过 (GGUF/GPTQ/AWQ)")
+
+    # 3. 架构筛选（排除 Qwen3.5 在 Iluvatar 上不支持）
+    log("\n--- 阶段3: 架构筛选 ---")
     arch_passed = []
     arch_rejected = 0
-    for m in all_models:
+    SKIP_ARCHS = ['qwen3_5', 'Qwen3_5', 'Qwen3.5']
+    for m in hf_models:
         ok, reason = check_architecture(m['model_id'])
-        if ok:
+        if not ok:
-            arch_passed.append(m)
-        else:
             arch_rejected += 1
-            log(f"  ✗ {m['model_id']}: {reason}")
+            log(f"  x {m['model_id']}: {reason}")
+            continue
+        arch_str = str(reason).upper()
+        if any(a.upper() in arch_str for a in SKIP_ARCHS):
+            arch_rejected += 1
+            log(f"  x {m['model_id']}: Qwen3.5(Iluvatar不支持)")
+            continue
+        arch_passed.append(m)
         time.sleep(0.15)
     log(f"架构筛选: {len(arch_passed)} 通过, {arch_rejected} 拒绝")
 
-    # 3. 按 GPU 筛选并提交
+    # 4. 筛选并提交（只针对目标GPU）
-    log("\n--- 阶段3: 筛选并提交 ---")
+    log(f"\n--- 阶段4: 筛选并提交 [{TARGET_GPU}] ---")
-    total_submitted = 0
+
-    for gpu in gpus:
+    # 检查队列
-        token = ACCOUNTS.get(gpu)
+    available = check_queue_available()
-        if not token:
+    if available <= 0:
+        log(f"  队列满，跳过")
+        return 0
+    log(f"  队列可用: {available}")
+
+    # 筛选
+    to_submit = []
+    for m in arch_passed:
+        model_id = m['model_id']
+
+        # 检查全平台验证状态
+        verify = check_platform_verify(model_id)
+        if TARGET_GPU in verify:
+            continue  # 已有记录，跳过
+
+        # 检查自己是否已提交
+        if check_my_submitted(model_id):
             continue
 
-        log(f"\n[{gpu}]")
+        # 检查是否已知失败（避免重复提交）
-
+        if is_model_failed(model_id):
-        # 检查队列
-        available = check_queue_available(gpu, token)
-        if available <= 0:
-            log(f"  队列满，跳过")
             continue
-        log(f"  队列可用: {available}")
 
-        # 筛选
+        to_submit.append(m)
-        to_submit = []
+        if len(to_submit) >= min(submit_limit, available):
-        for m in arch_passed:
+            break
-            model_id = m['model_id']
+        time.sleep(0.2)
 
-            # hygon 只接受 GGUF
+    log(f"  待提交: {len(to_submit)}")
-            if gpu == 'hygon_k100-ai' and 'GGUF' not in model_id.upper():
-                continue
 
-            # 检查全平台验证状态
+    # 提交
-            verify = check_platform_verify(model_id)
+    submitted = 0
-            if gpu in verify:
+    for m in to_submit:
-                continue  # 已有记录，跳过
+        ok, task_id, msg = submit_model(m['url'])
+        if ok:
+            submitted += 1
+            log(f"  ✅ {m['model_id']}")
+            db_conn.execute(
+                'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?)',
+                (m['model_id'], TARGET_GPU, str(task_id), datetime.now().isoformat())
+            )
+        else:
+            log(f"  ❌ {m['model_id']}: {msg}")
+            # 永久失败类型记录到 failed 表
+            if any(kw in str(msg) for kw in ['保护期', '白名单', '唯一性']):
+                record_failed(m['model_id'], msg)
+        time.sleep(0.5)
 
-            # 检查自己是否已提交
+    log(f"  提交完成: {submitted}/{len(to_submit)}")
-            if check_my_submitted(model_id, gpu, token):
-                continue
-
-            to_submit.append(m)
-            if len(to_submit) >= min(submit_limit, available):
-                break
-            time.sleep(0.2)
-
-        log(f"  待提交: {len(to_submit)}")
-
-        # 提交
-        submitted = 0
-        for m in to_submit:
-            ok, task_id, msg = submit_model(m['url'], gpu, token)
-            if ok:
-                submitted += 1
-                log(f"  ✅ {m['model_id']}")
-                db_conn.execute(
-                    'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?)',
-                    (m['model_id'], gpu, str(task_id), datetime.now().isoformat())
-                )
-            else:
-                log(f"  ❌ {m['model_id']}: {msg}")
-            time.sleep(0.5)
-
-        log(f"  提交完成: {submitted}/{len(to_submit)}")
-        total_submitted += submitted
 
     db_conn.commit()
     log(f"\n{'=' * 50}")
-    log(f"流程完成，共提交 {total_submitted} 个模型")
+    log(f"流程完成，共提交 {submitted} 个模型")
-    return total_submitted
+    return submitted
 
 
 # ============================================================
@@ -514,17 +496,16 @@ class AgentHandler(BaseHTTPRequestHandler):
             if content_len > 0:
                 body = json.loads(self.rfile.read(content_len))
 
-            gpus = body.get('gpus', list(GPU_CONFIGS.keys()))
+            limit = body.get('limit', 2)
-            limit = body.get('limit', 30)
 
-            self._json({'status': 'started', 'gpus': gpus, 'limit': limit})
+            self._json({'status': 'started', 'gpu': TARGET_GPU, 'limit': limit})
 
             # 后台运行
             def _run():
                 try:
                     state['running'] = True
                     state['last_run'] = datetime.now().isoformat()
-                    count = run_pipeline(gpus=gpus, submit_limit=limit)
+                    count = run_pipeline(submit_limit=limit)
                     state['last_result'] = {'submitted': count, 'success': True}
                 except Exception as e:
                     log(f"流程异常: {traceback.format_exc()}")
@@ -572,10 +553,9 @@ class AgentHandler(BaseHTTPRequestHandler):
 
         # 3. ModelHub 查询 API
         try:
-            token = list(ACCOUNTS.values())[0]
             resp = requests.get(
                 'https://modelhub.org.cn/api/adapt/task/page',
-                headers={'Xc-Token': token, 'Accept': 'application/json'},
+                headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'},
                 params={'current': 1, 'pageSize': 1, 'onlyMine': 'true'},
                 timeout=10,
             )
@@ -590,17 +570,16 @@ class AgentHandler(BaseHTTPRequestHandler):
 
         # 4. ModelHub 提交 API (dry test)
         try:
-            token = list(ACCOUNTS.values())[0]
             resp = requests.post(
                 'https://modelhub.org.cn/api/adapt/task/add',
-                headers={'Xc-Token': token, 'Accept': 'application/json', 'Content-Type': 'application/json'},
+                headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json', 'Content-Type': 'application/json'},
                 json={
                     'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
                     'taskType': 'text-generation',
-                    'targetGpu': 'Kunlunxin_p-800',
+                    'targetGpu': TARGET_GPU,
                     'framework': 'vllm',
                     'strategyId': STRATEGY_ID,
-                    'configParams': 'framework: vllm\n',
+                    'configParams': build_config_params(),
                 },
                 timeout=10,
             )
@@ -651,7 +630,7 @@ def main():
 
     log(f"智能体启动 | {HOST}:{PORT}")
     log(f"STRATEGY_ID: {STRATEGY_ID}")
-    log(f"GPU: {', '.join(GPU_CONFIGS.keys())}")
+    log(f"目标GPU: {TARGET_GPU}")
 
     # 启动后自动运行连通性测试
     def _startup_test():
@@ -694,10 +673,9 @@ def main():
 
         # 3. ModelHub 查询
         try:
-            token = list(ACCOUNTS.values())[0]
             resp = requests.get(
                 'https://modelhub.org.cn/api/adapt/task/page',
-                headers={'Xc-Token': token, 'Accept': 'application/json'},
+                headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json'},
                 params={'current': 1, 'pageSize': 1, 'onlyMine': 'true'},
                 timeout=10
             )
@@ -711,15 +689,14 @@ def main():
 
         # 4. ModelHub 提交
         try:
-            token = list(ACCOUNTS.values())[0]
             resp = requests.post(
                 'https://modelhub.org.cn/api/adapt/task/add',
-                headers={'Xc-Token': token, 'Accept': 'application/json', 'Content-Type': 'application/json'},
+                headers={'Xc-Token': TARGET_TOKEN, 'Accept': 'application/json', 'Content-Type': 'application/json'},
                 json={
                     'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
-                    'taskType': 'text-generation', 'targetGpu': 'Kunlunxin_p-800',
+                    'taskType': 'text-generation', 'targetGpu': TARGET_GPU,
                     'framework': 'vllm', 'strategyId': STRATEGY_ID,
-                    'configParams': 'framework: vllm\n',
+                    'configParams': build_config_params(),
                 }, timeout=10
             )
             data = resp.json()
@@ -736,7 +713,7 @@ def main():
         try:
             state['running'] = True
             state['last_run'] = datetime.now().isoformat()
-            count = run_pipeline(submit_limit=30)
+            count = run_pipeline(submit_limit=2)
             state['last_result'] = {'submitted': count, 'success': True}
         except Exception as e:
             log(f"流程异常: {traceback.format_exc()}")